ORIGINAL PAPER

Disturbance regimes in a wetland remnant: implications for trait-displacements and shifts in the assemblage structure of carabidbeetles (Coleoptera: Carabidae)

Giovanni Bettacchioli • Mauro Taormina •

Fabio Bernini • Massimo Migliorini

Received: 11 November 2010 / Accepted: 2 June 2011 / Published online: 14 June 2011

� Springer Science+Business Media B.V. 2011

Abstract Studies on disturbance regimes involving

carabid beetles have mainly focused on forest habitats. We

therefore decided to analyze the effects of disturbance on

carabid communities in a wetland remnant (Lake Chiusi,

central Italy). Results highlighted the presence of a dis-

turbance gradient affecting the species richness and trait-

displacement of carabid communities. Carabids were

sampled with pitfall traps from March to October 2008 at

nine randomly selected sample stations; a set of landscape

attributes were also collected. Principal Component Anal-

ysis (PCA) and generalized linear mixed models (GLMMs)

were used to link the distribution of carabid life-history

traits and species richness with the most informative

combination of landscape attributes. The first PCA axis

(PC1) showed significant correlation with ‘‘distance to the

lake shoreline’’ and ‘‘perimeter-area ratio’’, highlighting

the presence of a disturbance-axis. The second and third

axes accounted only for a trivial portion of the total vari-

ance. GLMMs revealed a progressive decrease in the

number of hygrophilous species from the core of the wet-

land to its outer areas. Similar trends were observed for

species richness and for predator species with good dis-

persal ability and larval period in summer. Our results

highlight the importance of taking into account commu-

nity-wide functional implications in landscape ecology

studies.

Keywords Ground beetles � Species sorting �Landscape ecology � AIC � Marsh

Introduction

Land-use modification is one of the main causes of habitat

loss and it is responsible for major modifications of the

environmental conditions (Magura et al. 2006). For envi-

ronments affected strongly by landscape simplification,

assemblage of species are expected to be structured by their

ability to reach upon this kind of disturbance (Lambeets

et al. 2008). One essential prerequisite to predict the effects

of disturbance regimes on population and communities is

to establish quantitative links between spatial patterns and

biodiversity (Purtauf et al. 2005). This is, however, very

difficult because the response of individual species to dis-

turbance may differ substantially according to life-history

strategies (Gaublomme et al. 2008). Opportunistic species

may be favored by their capacity to rapidly exploit avail-

able resources and newly vacated niches, whereas there is

generally a strong decline in the population size of sensi-

tive species, which may be driven to extinction (Abildsnes

and Tømmeros 2000).

Among arthropods, carabid beetles (Coleoptera: Cara-

bidae) are considered both useful environmental indicators

and targets for conservation efforts (Eversham et al. 1996;

Niemela 2001; Rainio and Niemela 2003; Gaublomme

et al. 2008). A great deal of information has been published

on the ecology and taxonomy of carabid beetles and their

sensitivity to disturbance has been documented for a wide

range of human activities. In particular habitat fragmenta-

tion, grazing, fertilization, sylvicultural practices and pes-

ticides seem to affect the abundance and species richness of

carabid assemblages (Lovei and Sunderland 1996; Holland

and Luff 2000; Niemela 2001; Rainio and Niemela 2003;

Niemela et al. 2007).

Analysis of the community structure of carabid beetles

is potentially misleading: species richness may be a too

G. Bettacchioli (&) � M. Taormina � F. Bernini � M. Migliorini

Department of Evolutionary Biology, University of Siena,

Via Aldo Moro 2, 53100 Siena, Italy

e-mail: bettacchiol2@unisi.it

123

J Insect Conserv (2012) 16:249–261

DOI 10.1007/s10841-011-9412-9

reductive index for interpreting habitat type, sensitivity to

human disturbance and the role of carabids in the eco-

system (Gobbi and Fontaneto 2008). Therefore placing

too much emphasis on the assemblage structure could

lead to the detection of apparent environmental changes.

For example, stochastic processes can promote changes in

assemblages of different species with similar functional

traits (Marchinko et al. 2004; Lambeets et al. 2008).

Moreover, ecosystem functioning depends more strongly

on functional diversity, i.e. the value and range of spe-

cies-traits, than on species richness per se (Barbaro and

van Halder 2009). As a consequence, life-history trait-

based methods have been developed in the last few years,

but few studies have attempted to describe relationships

between landscape attributes and species life-history traits

(Ribera et al. 2001; Barbaro and van Halder 2009). Pur-

tauf et al. (2005) demonstrated that landscape simplifi-

cation causes a more pronounced decline in predator

carabids than in phytophagous and omnivorous ones.

Barbaro and van Halder (2009) found that carabid beetles

more sensitive to fragmentation presented a key set of

traits: intermediate body size, spring adult activity and

summer breeding. In Italy, Gobbi and Fontaneto (2008)

found a negative relationship between the number of

brachypterous, large-sized carabids and the intensity of

human impact in agroecosystems of the Po River valley.

Carabid surveys aiming to assess human interference with

the landscape should focus on the distribution of life-

history traits across environmental gradients, thereby

allowing the generalization of results for both theoretical

and applied purposes (McGill et al. 2006; Gobbi and

Fontaneto 2008). Conserving functional diversity at the

landscape level may consequently help maintain large-

scale and long-term ecosystem processes (Tscharntke

et al. 2008).

Although land-use modification interest different types

of ecosystems, in the case of carabid beetles the focus has

mainly been on forest habitats (Niemela 2001; Hollmen

et al. 2007). As a result, there is a lack of information about

the effects of disturbance regimes on the life-history traits

of ground beetles in wetland ecosystems. This is an

important issue, because wetlands and their unique biota

are disappearing worldwide due to human activities (Gibbs

1995, 2001). For example, between 1865 and 1972 wet-

lands in Italy decreased by *75% because of water

extraction and drainage (Green et al. 2002).

The present work is based on the general idea that sets

of traits are related to the ability of species to cope with

habitats characterized by different degrees of disturbance

(Lambeets et al. 2008). The main objective of this study

was to link the distribution of carabid life-history traits and

species richness with the most informative combination of

landscape attributes.

In particular, the present study aimed to: (1) understand

which combination of landscape attributes significantly

affects carabid beetle assemblages in a wetland remnant,

(2) verify if it is still possible to identify a set of functional

life-history traits for carabid beetles that attest to their

adaptation to wetland conditions and (3) understand whe-

ther there is a trade-off between disturbance regimes and

the ecological importance of the species sorting

mechanism.

Materials and methods

Study area

The Lake Chiusi wetland is located in the south-eastern

Tuscany, along the boundary with the Umbria region

(800.21 ha; 43�301300 N, 11�4703700 E). It represents a

remnant of a wider marsh-lacustrine area that occupied at

least 140 km2 of the Chiana Valley during the sixteenth

century (Alexander 1984). Land reclamation started in the

Roman period and continued until the end of the eighteenth

century (Alexander 1984).

The lake surface is about 260 ha and is connected by an

artificial channel to nearby Lake Montepulciano. Today the

whole marsh-lacustrine system of Chiana Valley is

restricted to less than 12.8 km2, representing *9.1% of its

past surface. In the study area soils are homogeneously

loamy, and the mosaic landscape comprises strips of by

riparian vegetation (with Phragmites australis, Carex elata

and Carex riparia), small hygrophilous forests (with Salix

alba and Populus nigra), poplar stands, wet meadows and

pastures (with Eleocharis palustris, Galega officinalis,

Galium palustre, Pastinaca sativa, Geranium dissectum

and Daucus carota). The entire area is surrounded by an

intensive agricultural matrix.

In the year of the survey (2008) the total annual rainfall

was 964.6 mm. The minimum and maximum temperatures

were respectively -8.2 and 16.4� C in February and 12.3

and 35.6�C in August (meteorological station of Castigli-

one del Lago). In 2004 the Lake Chiusi wetland was

declared a ‘‘Special Area of Conservation’’ (SAC)

(Directive 92/43/EEC).

Sampling design

We performed a field recognition of the study area to check

for the accessibility of sites and than a simple random

sampling design was applied. Nine coordinate pairs (UTM

(ED50)) were randomly extracted using ‘‘Random points’’

procedure in Quantum GIS version 1.3.0. ‘‘Mimas’’

(Quantum GIS Development Core Team 2009). The

extracted coordinates were used to identify the center of the

250 J Insect Conserv (2012) 16:249–261

123

sample stations. For the extraction of the coordinates we

considered the total area covered by the main land-use

types in CORINE land cover 2000 (levels 3 and 4): inland

marshes (code: 4.1.1) (74.54 ha; 3 sample stations), pas-

tures (code: 2.3.1) (5.35 ha; 2 sample stations), agro-for-

estries (2.4.4) (5.12 ha; 1 sample station) and hygrophilous

forests (3.1.1.6) (65.06 ha; 3 sample station) (Fig. 1)

(Maricchiolo et al. 2005). To reduce the probability of non-

independent samples we established 100 m as a minimum

acceptable distance between the center of two sample

stations. Mantel test (Mantel 1967) indicated the absence of

spatial autocorrelation among the extracted sample stations

(R = 0.0027; P = 0.1926) (PAST, Paleontological Statis-

tics Software Package, Hammer et al. 2001).

Carabids were sampled using pitfall traps. Although

there are intrinsic biases to pitfall trapping which may

influence carabid catches (Topping and Sunderland 1992),

standardized pitfall trapping was considered a suitable

collection method for comparing patterns of assemblage-

wide species traits. Each trap consisted of a 550 ml poly-

ethylene beaker (ø 90 mm) filled with *300 ml of a

solution of salt and wine vinegar. Traps were covered with

a circular plastic roof (ø 200 mm) to prevent excessive rain

and litter from reaching them. At each sampling site five

pitfall traps were placed on the center and at the tips of an

imaginary greek cross. The central traps were positioned in

randomly selected coordinate pairs. To avoid interference,

the distance between the four peripheral traps and the

central one was about 10 m (Topping and Sunderland

1992).

Carabid communities were sampled from March to

October 2008, and pitfalls were emptied monthly. Carabid

beetles were identified using standard keys (Porta 1923;

Jeannel 1941, 1942; Trautner and Geigenmuller 1987) and

follow the nomenclature in Brandmayr et al. (2005).

Landscape attributes

A land-use map of the study area, based on the CORINE

land cover 2000 classification (levels 3 and 4), was used to

obtain a specific set of landscape attributes. For each

habitat patch hosting at least one sample station we cal-

culated: patch size (A) (in m2), perimeter-area ratio (c),

habitat heterogeneity in patch surroundings (H0), Proximity

Index (PX) and distance to the lake shoreline (in m). We

considered the distance of sample stations from the lake

shoreline because the drainage of standing water in the

study area has proceeded from the margin to the central

portion of the wetland (Alexander 1984). This measure was

related to the distance of sample stations from the core of

the wetland. In particular, we calculated the minimum

distance between the center of the sample stations from the

point where helophytic vegetation stopped and free water

started (minimum distance from lake: MDL). To obtain a

measure of the distance from the core of the wetland less

dependent on possible seasonal variations in water level,

we calculated the distance to the lake shoreline along the

segment joining the center of the sample stations with the

centroid of the lake (minimum distance from lake along

the station-centroid segment: MDC). The MDL varied

between 8 and 1,189 m, whereas the MDC ranged from 18

to 1,815 m.

The perimeter-area ratio (c) was calculated using the

formula c = 2HpA/P, where A is the patch area and P is

the patch perimeter. When c is close to one the patch is

almost circular, whereas when c is much less than one the

patch is narrow or elongated. To quantify habitat hetero-

geneity in patch surroundings (H0), we calculated the

Shannon-Wiener Index using the ratio between the length

of the focal patch edge bordering with a certain class of

land-use (in m) and the total perimeter of the focal patch.

The Proximity Index (PX) was used to distinguish the

sparse distribution of small habitat patches from clusters of

large patches (Gustafson and Parker 1994). PX values were

calculated for each habitat patch containing at least one

sample station (focal patch), identifying each habitat patch

i (with the same land-use type of the focal patch) whose

edge lies at least partially within a specified proximity

buffer of the focal patch. PX was calculated using area (Si)

and the edge-to-edge distance from patch i to its nearest-

neighbor habitat patch (zi) of each of the n habitat patches

identified within the buffer, including the focal patch:

Fig. 1 Study area and sampling design. The black line represents the

boundary of the Lake Chiusi SAC; light grey areas represent water

bodies, channels and flooded areas; dark grey areas represent the total

area covered by the main land-use types (CORINE land cover 2000

classification); white circles represent sample stations

J Insect Conserv (2012) 16:249–261 251

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Pni¼1 ðSiziÞ (Gustafson and Parker 1994). To calculate PX

we considered a circular buffer with a radius of 400 m

centered on the sample station. We used a 400 m radius

because this measure is considered greater than the mean

foraging or dispersal distance for most carabids (Barbaro

and van Halder 2009). The patch size (A) varied from 1.5 to

40 ha. We used Quantum GIS version 1.3.0. ‘‘Mimas’’ to

calculate all landscape attributes and the landscape metrics

needed to calculate PX and H0.

Species richness and species traits

To reduce the influence of rare and vagrant species, we

only considered species with at least ten individuals cap-

tured during the sampling period. Species richness (S) was

calculated as the average number of species caught at each

sampling station for each month of the sampling period.

Similarly, we calculated the average number of species

exhibiting the same life-history traits. We used 72 average

measurements (from nine sample stations and 8 months of

sampling) for every response variable. Preliminary PER-

MANOVA tests (Permutational Multivariate Analysis of

Variance; Anderson 2005) were executed to avoid the risk

of temporal pseudoreplication. For each species we col-

lected information regarding: (a) trophic group, (b) wing

development, (c) body size, (d) larval instar period and

(e) habitat preference (Table 1). Trophic group, larval

instar period and wing development were derived from

Brandmayr et al. (2005). Information on habitat preference

was obtained from Magistretti (1965), Casale et al. 1982

and Brandmayr et al. 2005. Body size categories were

established according to Vigna Taglianti et al. (1994).

Data analysis

To interpret and summarize major patterns of variation in

carabid communities we performed Principal Component

Analysis (PCA) on square root, centred and standardized

species data using CANOCO 4.5 program (ter Braak and

Smilauer 2002). The scree plot method was applied to

distinguish between ‘‘interpretable’’ and trivial components

(Jackson 1993). In our case the elbow of the scree plot line

was located on the horizontal axis, at the second principal

component (PC2), confirming that only the first principal

component (PC1) should be considered ‘‘interpretable’’.

PC1 was interpreted on the basis of Spearman rank

correlations between PC1 and the landscape attributes.

Spearman correlations were calculated using STATISTICA

5 (StatSoft, Inc., Tulsa, USA). The Bonferroni correction

was applied to identify statistically significant correlations.

Generalized linear mixed models (GLMMs) (McCol-

lough and Searle 2001) were used to identify patterns on

species richness and species traits. The average species

richness and the mean values of life-history traits repre-

sented the response variables, whereas the first principal

component (PC1) was considered to be a continuous

independent variable. The most reliable model was inferred

using the corrected form of Akaike’s information criterion

(AICc) based on model fit and model complexity criteria

(Burnham and Anderson 2002; Johnson and Omland 2004).

Error assumptions were checked prior to analysis, and

response variables were log(x ? 1)-transformed when

assumptions were not met. Coefficients of determination

(R2) were calculated to express the percentage of vari-

ability in the response variable explained by each model.

To correct for possible differences between habitats, the

habitat factor was included in the models as a random

factor. Because of the low number of visual predators (VP:

one species) and spermophagous species (SP: two species),

these two categories were merged respectively with tradi-

tional predators (response variable name: TPVP) and

omnivorous species (response variable name: OMSP)

before performing GLMMs. For the same reason the

number of forest generalist species (FOR) was not con-

sidered for GLMMs. No species with annual larval instar

(ANN) were found. GLMMs were performed using

‘‘nlme’’, ‘‘lme4’’ and ‘‘mass’’ packages in R 2.11.0 (R

Development Core Team 2010).

Results

General results

We collected 19,113 individuals, belonging to 81 species

(‘‘Appendix’’). By excluding rare and vagrant species from

statistical analysis, we were left with 19,009 individuals

belonging to 51 different species. Among the species

caught in significant number, 30 were hygrophilous species

(10,407 individuals), 10 were generalists species (5,088

individuals), 9 were open-habitat species (2,905 individu-

als) and only 2 were forest generalist species (609 indi-

viduals). The most abundant species were Pterostichus

anthracinus hespericus (2,295 individuals), Pterostichus

niger (2,115 individuals), Poecilus cupreus (1,815 indi-

viduals), Agonum duftschmidi (1,626 individuals), Carabus

granulatus interstitialis (1,548 individuals), Brachinus

crepitans (1,522 individuals) and Pterostichus melas itali-

cus (1,082 individuals).

PCA and Spearman correlations

Principal Component Analysis (PCA) revealed the preva-

lence of a disturbance-axis (PC1; eigenvalue: 0.425;

explanatory value: 70.6%). PC1 showed significant

252 J Insect Conserv (2012) 16:249–261

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negative correlations with distance to the lake shoreline

(MDL and MDC) and the area-perimeter ratio (c)

(Table 2). Spearman correlations confirmed that increasing

values of PC1 corresponded to decreasing distances from

the lake shoreline and to decreasing c values. The second

(PCA2; eigenvalue: 0.103; explanatory value: 3.4%) and

third (PCA3; eigenvalue: 0.084; explanatory value: 5.1%)

principal components were related to patch size (A), habitat

heterogeneity in patch surroundings (H0) and Proximity

Index (PX) but showed low eigenvalues and explained only

a small portion of the total variance.

Generalized linear mixed models

Both species richness (S) and most of the carabid life-

history traits were significantly influenced by the distur-

bance-axis (PC1) (Table 3). Species richness (S) peaked at

short distances from the lake shoreline and low c values

(Fig. 2), but the AICc value for S was higher than that for

other models (AICc(S) = 309.9). The number of predator

species (TPVP) showed the same trend as species richness

(Fig. 3a), although the model fit for TPVP was better than

that for S (R2(TPVP) = 0.7723; R2(S) = 0.5660). The

number of omnivorous and spermophagous species

(OMSP) peaked at low PC1 values and slightly decreased

with increasing values of PC1 (Fig. 3b).

Macropterous (M) and wing-dimorphic (D) species

showed a preference for areas near the lake shoreline

(Fig. 4a, b). Although the average number of brachypter-

ous species (B) was always very low (never more than four

Table 1 Classification of life-history traits

Life-history trait Description Response

variable

Trophic group

Traditional predator Predator species that recognize their prey using mainly olfactory and tactile stimuli TP

Visual predator Predator species that recognize their prey using mainly visual stimuli VP

Omnivorous species Predator species that supplement their diet with the seeds of herbaceous plants OM

Spermophagous species Species that feed on the seeds of herbaceous plants SP

Wing development

Macropterous species Species with fully developed hind wings M

Brachypterous species Species with reduced or absent hind wings B

Wing-dimorphic species Species in which only part of the population is fully winged D

Body size

Large-sized species Species with body lengths exceeding 12 mm LAR

Medium-sized species Species with body lengths ranging from 6 to 12 mm MED

Small-sized species Species with body lengths shorter than 6 mm SMA

Larval instar period

Species with larval period in summer Spring breeder or species that breed in the first part of the summer season SUM

Species with larval period in winter Autumn breeder or species that breed at the end of the summer season WIN

Species with annual larval period Species whose larval instar lasts longer than 12 months ANN

Habitat preference

Hygrophilous species Species occurring in wetland habitats and in riparian zones HYG

Forest generalist species Species occurring in many forest types FOR

Generalist species Species occurring in both open landscapes and forests GEN

Open-habitat species Species occurring in meadows, pastures and crops OPE

The second column contains a brief description of each life-history trait. The third column lists the name of the variable associated with each life-

history trait

Table 2 Spearman correlations between the measured landscape

attributes and the disturbance-axis (PC1)

Variable measured rs P level

A -0.0042 0.9715

c -0.4168 0.002*

H0 -0.0429 0.7200

PX 0.0421 0.7249

MDL -0.7408 0.001*

MDC -0.8168 0.001*

rs Spearman’s rank correlation coefficient, A patch size, c perimeter-

area ratio, H0 habitat heterogeneity in patch surroundings, PX prox-

imity index, MDL minimum distance from lake, MDC minimum

distance from lake along the station-centroid segment

* Significant P-level after Bonferroni correction

J Insect Conserv (2012) 16:249–261 253

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species), these species were more frequent far away from

the lake shoreline and in patches with a circular shape

(Fig. 4c).

Large-(LAR), medium-(MED) and small-(SMA) sized

species (Fig. 5a–c) increased significantly with increasing

PC1 values. Among the tree body size categories, only the

number of small species showed a relatively low AICc

(AICc(SMA) = 44.1) value and a relatively high R2

(R2(SMA) = 0.6172). The average number of small spe-

cies was always very low (never more than five species).

The number of species with larval period in summer

(SUM) (Fig. 6) peaked at high PC1 values, whereas no

significant pattern (P C 0.05) was observed for the number

of species with larval period in winter (INV). The lowest

AICc value (11.8) and highest R2 (0.8162) were observed

for the number of hygrophilous species (HYG) (Fig. 7),

which showed a linear increase along the disturbance-axis.

The number of generalist (GEN) and open-habitat species

(OPE) showed no significant patterns.

Discussion

Our study suggests the presence of a disturbance gradient

affecting both assemblage structure and trait-displacement

of carabid beetles. In our study area the distance from the

core of the wetland (MDL and MDC) and the area-

perimeter ratio (c) were the landscape attributes that best

defined the first principal component of PCA (PC1). Patch

shapes varied in relation to distance from the lake

shoreline (Spearman’s rs between MDC and c = 0.333;

P = 0.004). Helophytic vegetation and hygrophilous for-

ests formed narrow vegetation strips along the perimeter

of the lake, resulting in low perimeter-area ratios. In

contrast, patches in the anthropogenic mosaic landscape

tended to have higher c values. PCA revealed the pres-

ence of a strong environmental gradient structuring the

characteristics of the sites and consequently the species

occurring in them. The disturbance-axis (PC1) accounted

for two sources of environmental disturbance: natural and

anthropogenic disturbance. The first source of disturbance

was higher near the wetland core and was represented by

hydrological instability i.e. the rapid variation of soil

water content (Brandmayr et al. 2005). This type of nat-

ural disturbance was closely related to seasonal oscilla-

tions in water level and to the temporary flooding of

areas. Anthropogenic disturbance, represented by land-use

modification associated with land reclamation, was higher

far away from the core of the wetland. Drainage of

standing waters in the Lake Chiusi wetland increased the

hydrological stability of the area, such that the original

marsh was converted into pastures, meadows and crops

(Alexander 1984). This radical transformation of envi-

ronmental conditions probably has had an important

impact on the structural and functional diversity of cara-

bid assemblages.

Table 3 Influence of the disturbance-axis (PC1) on the species

richness and life-history traits of carabid assemblages

Response variable F(1,67) P level AICc R2

S 66.87 0.001*** 309.9 0.5660

Trophic group

TPVP 165.79 0.001*** 277.3 0.7723

log(OMSP ? 1) 4.24 0.0434* 47.4 0.1910

Wing development

M 67.43 0.001*** 266.7 0.5987

B 4.10 0.0467* 186.1 0.2452

D 81.83 0.001*** 152.4 0.6816

Body size

LAR 33.19 0.001*** 226.4 0.3718

MED 30.50 0.001*** 226.9 0.1399

log(SMA ? 1) 23.62 0.001*** 44.1 0.6172

Larval instar period

SUM 94.74 0.001*** 285.2 0.6563

WIN 0.04 0.8253 95.3 0.0028

Habitat preference

log(HYG ? 1) 108.91 0.001*** 11.8 0.8162

GEN 1 0.3194 209.9 0.0134

log(OPE ? 1) 0 0.9931 23.6 0.3992

Generalized linear mixed model (GLMM) regression statistics (F; P-

level and R2) and the corrected Akaike information criterion (AICc)

are reported for average data

*** P B 0.001; ** 0.001 \ P \ 0.01; * P \ 0.05

0

2

4

6

8

10

12

14

16

18

20

-1.5 -1 -0.5 0 0.5 1 1.5 2

PC1

S

Fig. 2 Relationship between the average species richness (S) and the

‘‘disturbance-axis’’ (PC1). The principal component scores of PC1,

determined through Principal Component Analysis, are reported

along the x-axis

254 J Insect Conserv (2012) 16:249–261

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Species richness was greater near the core of the wetland

due to coexisting specialized and opportunistic species in

sites near the lake shoreline. The wetland core hosted many

hygrophilous species of the subfamilies Carabinae, Pter-

ostichinae, Platyninae, Brachininae and Chlaeniinae

(‘‘Appendix’’), as well as a great abundances of generalist

species such as Poecilus cupreus (1,799 individuals),

Pseudophonus rufipes (289 individuals) Nebria brevicollis

(284 individuals) and Trechus quadristriatus (132 indi-

viduals). It is known that small, periodic oscillations in

water level and local flood events represent a short-term

disturbance that may favour specialized species (Roth-

enbucher and Schaefer 2006; Lambeets et al. 2008). This is

because specialized species are able to cope with tempo-

rary changes in habitat conditions, reappearing quickly

after short disturbance events or benefiting from newly

created structural elements and microhabitats (Weigmann

0

2

4

6

8

10

12

14

16

18

20

(a)

PC1

TP

VP

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

-1.5 -1 -0.5 0 0.5 1 1.5 2

-1.5 -1 -0.5 0 0.5 1 1.5 2

PC1

log

(OM

SP

+1)

(b)

Fig. 3 Relationship between trophic groups and the disturbance-axis

(PC1). a Average number of predator species (TPVP); b log(x ? 1) of

the average number of omnivorous and spermophagous species

(OMSP)

0

2

4

6

8

10

12

14

(a)

PC1

M

0

1

2

3

4

5

6

PC1

D

0

1

2

3

4

-1.5 -1 -0.5 0 0.5 1 1.5 2

-1.5 -1 -0.5 0 0.5 1 1.5 2

-1.5 -1 -0.5 0 0.5 1 1.5 2

PC1

B

(b)

(c)

Fig. 4 Relationship between wing developments and the disturbance-

axis (PC1). a Average number of macropterous species (M); b average

number of wing-dimorphic species (D); c average number of

brachypterous species (B)

J Insect Conserv (2012) 16:249–261 255

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and Wohlgemuth-von Reiche 1999; Rothenbucher and

Schaefer 2006; Lambeets et al. 2008). In contrast, gener-

alist species have structural features that allow them to

cope with several types of habitat and to persist thanks to

repeated colonization events (Ribera et al. 2001; Lambeets

et al. 2008). This interpretation of the species richness

pattern is supported by the fact that generalist eurytopic

species were not affected by the disturbance-axis, whereas

the number of hygrophilous species decreased rapidly in

sites distant from the lake shoreline. Moreover, the high

edge-to-area ratio of the sites near the lake shoreline, plus

the marked contrast with the agricultural matrix, may have

favoured a massive influx of generalist species from the

anthropogenic matrix to the wetland core (Usher et al.

1993; Niemela 2001). For these reasons, the disturbance-

0

1

2

3

4

5

6

7

PC1

LAR

0

1

2

3

4

5

6

7

PC1

ME

D

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

-1.5 -1 -0.5 0 0.5 1 1.5 2

-1.5 -1 -0.5 0 0.5 1 1.5 2

-1.5 -1 -0.5 0 0.5 1 1.5 2

PC1

log

(SM

A+

1)

(a)

(b)

(c)

Fig. 5 Relationship between body size and the disturbance-axis

(PC1). a Average number of large-sized species (LAR); b average

number of medium-sized species (MED); c log(x ? 1) of the average

number of small-sized species (SMA)

0

2

4

6

8

10

12

14

16

-1.5 -1 -0.5 0 0.5 1 1.5 2

PC1

SU

M

Fig. 6 Relationship between the average number of species with

larval period in summer (SUM) and the disturbance-axis (PC1)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

-1.5 -1 -0.5 0 0.5 1 1.5 2

PC1

log

(HY

G+

1)

Fig. 7 Relationship between log(x ? 1) of the average number of

hygrophilous species (HYG) and the disturbance-axis (PC1)

256 J Insect Conserv (2012) 16:249–261

123

axis can be considered the main diagonal of the habitat

templet of Southwood (1988), with more unpredictable but

more favourable habitats on one side and more permanent

but highly unfavourable habitats on the other (Korfiatis and

Stamou 1999).

The observed species richness pattern seems to be sig-

nificantly different from that pointed out by Hollmen et al.

(2007). In Finland these authors found a higher species

richness in drained sites than in mires in their natural state.

However, most of the carabid species caught in the drained

mires were forest succession generalists, suggesting that

profound changes in environmental conditions had occur-

red (Hollmen et al. 2007). Comparison between our results

and those of Hollmen et al. (2007) should be made with

extreme caution because environmental conditions in

Mediterranean wetlands differ from those in Northern

European ones (Stamou 1998; Hollmen et al. 2007).

Most life-history traits were not randomly distributed in

the study area, indicating that species with different origin,

and therefore likely to behave independently showed the

same type of response to the same environmental gradient

(Ribera et al. 2001). The quantitatively most important

relationship was that of hygrophilous species with the

disturbance-axis. This finding is consistent with that of

Bonn and Kleinwachter (1999) and of Bezdek et al. (2006).

These authors found a significant decline in the number of

hygrophilous and specialized species with increasing dis-

tance to the waterline of the River Elbe (Bonn and

Kleinwachter 1999) and to the center of Mrtvy’ luh bog

(Bezdek et al. 2006). Moisture is known to be a key factor

in driving the species composition of carabid assemblages,

especially in the case of species associated with wetland

and riparian habitats (Thiele 1977; Frambs 1990; Horn and

Ulyshen 2009). Since the core of the wetland harboured a

higher number of hygrophilous species, this means that

only a narrow strip of vegetation around the lake main-

tained environmental conditions typical of wetland habi-

tats. This finding is also supported by the interpretation of

other assemblage-wide shifts in species traits.

The trends observed for trophic group categories suggest

changes in the amount and/or in the availability of specific

food resources (Andersen 2000; Purtauf et al. 2005; Bar-

baro and van Halder 2009). Along the disturbance-axis

there was a higher number of predator species in sites near

the lake shoreline. Spermophagous and omnivorous species

were more frequent in sites distant from the core of the

wetland. In the case of carabid beetles, predator species are

known to be more sensitive to land simplification than

spermophagous and omnivorous species because they

depend on a variety of habitats for the provision of alter-

native food sources (Toft and Bilde 2002; Purtauf et al.

2005; Gobbi and Fontaneto 2008). Moreover, spermopha-

gous species may be more abundant in anthropogenic

habitats because of the loss of potentially predaceous

ground beetles, whereas omnivorous species are opportu-

nistic generalists less sensitive to landscape modifications

(Andersen 2000; Purtauf et al. 2005; Gobbi and Fontaneto

2008).

The high hydrological instability of sites near the wet-

land core seems to affects the dispersal power, i.e. the wing

development, of carabid beetles. Macropterous and wing-

dimorphic species are better dispersers than brachypterous

species because they are able to escape and rapidly

re-colonize flooded areas by flying (Ribera et al. 2001;

Zalewski and Ulrich 2006; Lambeets et al. 2008). This

probably explains the high number of macropterous and

wing-dimorphic species in sites near the core of the wet-

land and the weak but significant increase in brachypterous

species in hygrophilous forest patches distant from the lake

shoreline.

Larger species are generally linked to less disturbed

habitats because of their long life cycles and their poor

dispersal ability (Blake et al. 1994; Rainio and Niemela

2003; Kotze and O’Hara 2003; Jelaska and Durbesic 2009).

In contrast, the faster development and shorter generation

time of smaller carabid species enable them to cope with

stressful and unstable conditions (Blake et al. 1994; Kotze

et al. 2003; Jelaska and Durbesic 2009). In this study we

found a significant decrease in all body size categories

moving from the core to the outer areas of the wetland, but

the decrease was more pronounced for the smaller species.

This findings is apparently inconsistent with the body size

predictions cited above. However, many large species

caught in sites near the lake shoreline were hygrophilous,

predator species with a high dispersal power (macropterous

or wing-dimorphic) and with larval period in summer.

They were therefore well adapted to wetland habitats.

Similarly, many small-sized species found in wetland core

sites belonged to the genera Paratachys, Ocys, Asaphidion,

Emphanes, Trepanes and Philochthus, i.e. all species pre-

ferring wetlands habitats and riparian zones (Magistretti

1965; Brandmayr et al. 2005; Lambeets et al. 2008).

As for the period of larval instar, we found a higher

number of carabid species with larval period in summer in

sites near the wetland core. This type of development does

not involve a period of larval dormancy and is generally

considered an opportunistic feature (Ribera et al. 2001;

Barbaro and van Halder 2009). In wetland habitats, instead,

carabid beetles with larval period in summer are favoured

because they reduce larval mortality due to spring and

autumn flood events (Casale et al. 1993; Matalin 2007).

In conclusion, along the disturbance-axis there were

consistent shifts in traits in response to species sorting

rather than shifts in taxonomically different species with

similar functional traits. Our data suggest that eurytopic

species are constantly present also in less managed areas.

J Insect Conserv (2012) 16:249–261 257

123

However, dispersal of more specialized species might be

important at smaller spatial scale, in that it allows them to

re-colonize or abandon temporarily flooded areas (Kraus

and Morse 2005; Lambeets et al. 2008).

The common response to the same environmental gra-

dient may allow the definition of functional groups which

can be used to characterize functional diversity and its

relationship with land-use modifications (Ribera et al.

2001). Sites near the wetland core hosted mainly

hygrophilous, predator species with good dispersal ability

and larval period in summer. Considering this set of life-

history traits in the context of the habitat templet theory,

we can assume the limited presence, in the Lake Chiusi

SAC, of carabid assemblages well adapted to wetland

conditions. From a conservation perspective, we recom-

mend the implementation of temporarily flooded areas and

the restoration of riparian habitats for the maintenance of

assemblage-wide functional properties as well as the con-

servation of specialized carabid species.

Acknowledgments We thank Dr. Claudia Angiolini, Dr. Marco

Landi, Dr. Flavio Frignani and Dr. Tommaso Giallonardo for pro-

viding land-use data on the Lake Chiusi SAC. We are especially

grateful to Andrea Petrioli for help in determining carabid beetles. We

also would like to thank Dr. Arabella Palladino for the linguistic

revision of the manuscript and the two anonymous referees for

valuable comments on the first draft of the manuscript. This work was

supported by a grant from University of Siena (PAR).

Appendix

See Table 4.

Table 4 Life-history traits of carabid species caught in the study area

Species Trophic

group

Wing

development

Body

size

Larval

period

Habitat

preference

Species used for data analysis

Brachinus crepitans (Linne, 1758) TP D MED SUM OPE

Brachinus plagiatus Reiche, 1858 TP M MED SUM HYG

Brachinus psophia Audinet-Serville, 1821 TP M MED SUM HYG

Brachinus sclopeta (Fabricius, 1792) TP M MED SUM HYG

Brachinus italicus (Dejean, 1831) TP B MED SUM HYG

Carabus granulatus interstitialis Duftschmid 1812 TP D LAR SUM HYG

Carabus rossii Dejean, 1826 TP B LAR SUM GEN

Carabus violaceus picenus A. Villa and G.B. Villa, 1838 TP B LAR WIN GEN

Leistus fulvibarbis Dejean, 1826 TP M MED WIN FOR

Nebria brevicollis (Fabricius, 1792) TP M MED WIN GEN

Clivina fossor (Linne, 1758) TP D MED SUM GEN

Trechus quadristriatus (Schrank, 1781) TP M SMA WIN GEN

Paratachys bistriatus Duftschmid 1812 TP M SMA SUM HYG

Ocys harpaloides (Audinet-Serville, 1821) TP M SMA SUM HYG

Asaphidion flavipes (Linne, 1761) VP M SMA SUM HYG

Emphanes arillaris occiduus (Marggi and Huber, 2001) TP M SMA SUM HYG

Trepanes assimilis (Gyllenhal, 1810) TP M SMA SUM HYG

Philochthus inoptatus (Schaum, 1857) TP M SMA SUM HYG

Philochthus lunulatus (Geffroy in Fourcroy, 1785) TP M SMA SUM HYG

Stomis pumicatus (Panzer, 1796) TP B MED SUM HYG

Poecilus cupreus (Linne, 1758) TP M LAR SUM GEN

Pterostichus vernalis (Panzer, 1796) TP M MED SUM HYG

Pterostichus strenuus (Panzer, 1796) TP D SMA SUM FOR

Pterostichus elongatus (Duftschmid, 1812) TP M LAR SUM HYG

Pterostichus niger (Schaller, 1783) TP M LAR WIN HYG

Pterostichus anthracinus hespericus(Bucciarelli and Sopracordevole, 1958)

TP D LAR SUM HYG

Pterostichus nigrita (Paykull, 1790) TP M LAR SUM HYG

Pterostichus melas italicus (Dejean,1828) TP B LAR WIN GEN

Chlaeniellus nigricornis (Fabricius, 1787) TP M LAR SUM HYG

258 J Insect Conserv (2012) 16:249–261

123

Table 4 continued

Species Trophic

group

Wing

development

Body

size

Larval

period

Habitat

preference

Chlaenius chrysocephalus (P. Rossi, 1790) TP M MED SUM HYG

Oodes helopioides (Fabricius, 1792) TP M MED SUM HYG

Badister sodalis (Duftschmid, 1812) TP M MED SUM HYG

Anisodactylus binotatus (Fabricius, 1787) OM M LAR SUM HYG

Stenolophus mixtus (Herbst, 1784) OM M SMA SUM HYG

Ophonus diffinis (Dejean, 1829) SP M MED WIN HYG

Ophonus puncticollis (Paykull, 1798) SP M MED WIN OPE

Pseudophonus rufipes (De Geer, 1774) OM M LAR WIN GEN

Harpalus dimidiatus (P. Rossi, 1790) OM M LAR SUM OPE

Harpalus distinguendus (Duftschmid, 1812) OM M LAR SUM OPE

Harpalus flavicornis Dejean, 1829 OM M MED SUM OPE

Harpalus oblitus Dejean, 1829 OM M LAR SUM HYG

Harpalus serripes (Quensel in Schonherr, 1806) OM M MED SUM OPE

Harpalus tardus (Panzer, 1797) OM M MED SUM GEN

Parophonus planicollis (Dejean, 1829) OM M MED SUM OPE

Calathus fuscipes (Goeze, 1777) TP D MED WIN GEN

Calathus circumseptus Germar, 1824 TP D MED WIN HYG

Agonum duftschmidi J. Schmidt, 1994 TP M MED SUM HYG

Anchomenus dorsalis (Pontoppidan, 1763) TP M MED SUM HYG

Oxypselaphus obscurus (Herbst, 1784) TP D SMA SUM HYG

Syntomus obscuroguttatus (Duftschmid, 1812) TP M SMA SUM GEN

Microlestes luctuosus Holdhaus in Apfelbeck, 1904 TP M SMA SUM OPE

Species excluded from data analysis

Calosoma maderae (Fabricius, 1775) TP M LAR WIN OPE

Carabus clatratus antonellii Luigioni,1921 TP D LAR SUM HYG

Carabus coriaceus Linne, 1758 TP B LAR WIN GEN

Cychrus italicus Bonelli,1810 TP B LAR WIN FOR

Notiophilus quadripunctatus Dejean, 1826 VP D SMA SUM OPE

Notiophilus rufipes Curtis, 1829 VP D SMA SUM HYG

Notiophilus substriatus G.R. Waterhouse, 1833 VP D SMA SUM HYG

Dyschiriodes chalybaeus (Putzeis, 1846) TP M SMA SUM HYG

Ocys quinquestriatus (Gyllenhall, 1810) TP M SMA SUM HYG

Trepanes articulatus (Panzer, 1796) TP M SMA SUM HYG

Sinechostictus dahlii (Dejean, 1831) TP M SMA SUM HYG

Pterostichus macer (Marsham, 1802) TP M LAR SUM HYG

Amara aenea (De Geer, 1774) OM M MED SUM OPE

Amara anthobia A. Villa and G.B. Villa, 1833 OM M MED SUM HYG

Amara similata (Gyllenhall, 1810) OM M MED SUM OPE

Chlaeniellus tristis (Schaller, 1783) TP M LAR SUM HYG

Chlaenius spoliatus (P.Rossi, 1792) TP M LAR SUM HYG

Chlaenius velutinus (Duftschmid, 1812) TP M LAR SUM HYG

Callistus lunatus (Fabricius, 1775) TP M MED SUM OPE

Badister bullatus (Schrank, 1798) TP M MED SUM HYG

Scybalicus oblongiusculus (Dejean, 1829) OM M LAR WIN OPE

Ophonus brevicollis (Audinet-Serville, 1821) SP M MED WIN OPE

Parophonus maculicornis (Duftschmid, 1812) OM M MED SUM HYG

Tschitscherinellus cordatus (Dejean 1825) SP M LAR WIN OPE

J Insect Conserv (2012) 16:249–261 259

123

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Habitat

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